Audio fingerprinting is premised upon the ability to link unlabeled/unencoded audio to corresponding metadata to determine information such as song artist, or song name. Audio fingerprinting is known for providing such information regardless of an audio format that is being used. Generally speaking, audio fingerprinting systems are content-based audio identification system that extract acoustically relevant characteristics of a portion of audio content (i.e., fingerprint, signature, etc.), and store it in a central database. When presented with unlabeled audio, its fingerprint is calculated and matched against those stored in the central database. Using the fingerprints and matching algorithms, even distorted versions of a single recording can be identified as the same music title. Other terms for audio fingerprinting are robust matching, robust or perceptual hashing, passive watermarking, automatic music recognition, content-based audio signatures and content-based audio identification. Areas relevant to audio fingerprinting include information retrieval, pattern matching, signal processing, databases, cryptography and audio cognition, among others.
Audio fingerprinting may be distinguished from other systems used for identifying audio content, such as audio watermarking. In audio watermarking (or encoded signature recognition), analysis on psychoacoustic properties of the audio signal must be conducted so that ancillary data representing a message (or watermark) can be embedded in audio without altering the human perception of sound. The identification of data relating to the audio is accomplished by extracting the message embedded in the audio. In audio fingerprinting, the message is automatically derived from the perceptually most relevant components of sound in the audio.
Audio fingerprinting has numerous advantages, one of which is that the fingerprinting may be used to identify legacy content, i.e., unencoded content. In addition, fingerprinting requires no modification of the audio content itself during transmission. As a drawback however, the computational complexity of fingerprinting is generally higher than watermarking, and there is a need to connect each device to a fingerprint repository for performing substantial fingerprint processing.
Accordingly, there is a need in the art to simplify the processing of audio fingerprint recognition. Additionally, there is a need to decentralize the process of fingerprint recognition, and provide efficient distribution of fingerprint recognition, particularly for large-scale systems.